A combination of p300 and Braf expression in the diagnosis and prognosis of melanoma
Bibliographic record
Abstract
BACKGROUND: To date only a handful of drugs are available for the treatment of melanoma. Among them vemurafenib, a BrafV600E specific inhibitor, showed promising results in terms of response rate and increase in median survival time. However, its effectiveness is limited by development of resistance and the search for additional drugs for melanoma treatment is ongoing. The present study was performed to analyze the correlation between Braf expression and the expression of p300, a known down stream target of the mitogen activated protein kinase (MAPK) pathway, which was recently shown by us to be a prognostic marker for melanoma progression and patient survival. METHODS: The expression of Braf and p300 expression were correlated and analyzed by Chi-square test. A total of 327 melanoma patient cases (193 primary melanoma and 134 metastatic melanoma) were used for the study. Classification & regression tree (CRT), Kaplan-Meier, and multivariate Cox regression analysis were used to elucidate the significance of the combination of Braf and p300 expression in the diagnosis and prognosis of melanoma. RESULTS: Our results demonstrate that Braf expression is inversely correlated with nuclear p300 and positively correlated with cytoplasmic p300 expression. Braf and cytoplasmic p300 were found to be associated with melanoma progression, tumor size and ulceration status. CRT analysis revealed that a combination of Braf and p300 expression (nuclear and cytoplasmic), could be used to distinguish between nevi and melanoma, and primary from metastatic melanoma lesions. The combination of Braf and nuclear p300 was significantly associated with patient survival and nuclear p300 was found to be an independent predictor of patient survival. CONCLUSION: Our results indicate a cross-talk between Braf and p300 in melanoma and demonstrate the importance Braf and p300 expression in the diagnosis and prognosis of melanoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".